plot.medsens {mediation} | R Documentation |
Plots results from medsens function. Y axis plots mediation effect and x-axis plots the error correlation rho. Standard options for plot function available.
## S3 method for class 'medsens': plot(x, xlab=NULL, ylab=NULL, xlim=NULL, ylim=NULL, main=NULL, pr.plot=FALSE,...) # ## S3 method for class 'plot.medsens': print(z)
x |
Output from medsens function. |
xlab |
x-axis label. |
ylab |
y-axis label. |
xlim |
range for x-axis. |
ylim |
range for y-axis. |
main |
main title for graph. |
pr.plot |
If pr.plot=TRUE then proportion mediated will be plotted. |
... |
Additional arguments to be passed. |
Luke Keele, Ohio State University, keele.4@osu.edu , Dustin Tingley, Princeton University, dtingley@princeton.edu, Teppei Yamamoto, Princeton University, tyamamot@princeton.edu, Kosuke Imai, Princeton University, kimai@princeton.edu
Imai, Kosuke, Luke Keele and Dustin Tingley (2009) A General Approach to Causal Mediation Analysis. Imai, Kosuke, Luke Keele and Teppei Yamamoto (2009) Identification, Inference, and Sensitivity Analysis for Causal Mediation Effects.
See also medsens
#Example with JOBS II Field experiment #For illustration purposes simulations set to low number. #Example with JOBS II Field experiment data(jobs) ## Not run: ######################################### #continuous mediator and continuous outcome ######################################### #fit parametric model model.m <- lm(job_seek ~ treat + depress1 + econ_hard + sex + age + occp + marital + nonwhite + educ + income, data=jobs) model.y <- lm(depress2 ~ treat + job_seek + depress1 + econ_hard + sex + age + occp + marital + nonwhite + educ + income, data=jobs) #pass model objects through medsens function sens.cont <- medsens(model.m, model.y, T="treat", M="job_seek", INT=FALSE, DETAIL=FALSE, sims=1000) #plot mediation effect and 95 plot(sens.cont, main="JOBS", ylim=c(-.2,.2)) ## End(Not run)